A neural model for multi-expert architectures

We present a generalization of conventional artificial neural networks that allows for a functional equivalence to multi-expert systems. The new model provides an architectural freedom going beyond existing multi-expert models and an integrative formalism to compare and combine various techniques of learning. (We consider gradient, EM, reinforcement, and unsupervised learning.) Its uniform representation aims at a simple genetic encoding and evolutionary structure optimization of multi-expert systems. This paper contains a detailed description of the model and learning rules, empirically validates its functionality, and discusses future perspectives.

• Type: Journal
• Year: 2002
• Where: CORR
• Authors: Marc Toussaint
• Keywords: Artificial Neural Networks, CORR 2002, Education, Multi-expert Models, Multi-expert Systems